ReviewSystematic biology2015
Cancer evolution: mathematical models and computational inference.
Review in Systematic biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 184 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
184 citing papers in PubMed, 1 synthesis or guideline pooled it, 395 citations in OpenAlex.
- Evolutionary perspectives, heterogeneity and ovarian cancer: a complicated tale from past to present.Journal of ovarian research · 2022Pooled it
- Longitudinal Liquid Biopsy and Mathematical Modeling of Clonal Evolution Forecast Time to Treatment Failure in the PROSPECT-C Phase II Colorectal Cancer Clinical Trial.Cancer discovery · 2018Trial
- Condensates and cell states: A new paradigm for understanding tumor biology.Biophysical journal · 2026Review
- A guide to understanding tumour evolution through the lens of population genetics.Nature reviews. Cancer · 2026Review
- Statistical Inconsistency of Error-correction Objectives for Perfect Phylogenies.bioRxiv : the preprint server for biology · 2026Article
- Bayesian inference of tissue-migration histories in metastatic cancer from cell-lineage tracing data.Cell genomics · 2026Article
- A new cancer progression model: From synthetic tumors to real data and back.PLoS computational biology · 2026Article
- Stochastic Evolutionary Control in Heterogeneous Populations.bioRxiv : the preprint server for biology · 2026Article
- Selective sweep probabilities in spatially expanding populations.Nature communications · 2026Article
- Co-mapping clonal and transcriptional heterogeneity in somatic evolution via GoT-Multi.Cell genomics · 2026Article
- 75 Years of Mathematical Oncology.bioRxiv : the preprint server for biology · 2026Article
- Article
- A hybrid Ornstein-Uhlenbeck-Branching framework unifies microbial and pediatric tumor evolution.Frontiers in oncology · 2026Article
- SISTEM: simulation of tumor evolution, metastasis, and DNA-seq data under genotype-driven selection.Bioinformatics (Oxford, England) · 2025Article
- MyClone: rapid and precise reconstruction of clonal population structures for tumors.BMC bioinformatics · 2025Article
- Sc-TUSV-Ext: Single-Cell Clonal Lineage Inference from Single Nucleotide Variants, Copy Number Alterations, and Structural Variants.Journal of computational biology : a journal of computational molecular cell biology · 2025Article
- Bayesian inference of fitness landscapes via tree-structured branching processes.Bioinformatics (Oxford, England) · 2025Article
- Review
- Review
- CINner: Modeling and simulation of chromosomal instability in cancer at single-cell resolution.PLoS computational biology · 2025Article
124 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.
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Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.